About the position

Alchemy is seeking a qualified Practitioner with applied, real-world experience in Generative AI Integration for Developers to participate in a skills assessment validation engagement. This is a short-term, contract, remote engagement in which the Practitioner will complete a practitioner-level skills assessment and a brief post-assessment survey. This role does not involve teaching, instructional design, content creation, or ongoing advisory responsibilities.

Engagement Details

• Engagement Type: Contract / 1099 – Short-term engagement
• Location: Remote
• Estimated Item Count: ~90
• Estimated Time to Completion: Approximately 1.5–2.5 hours
• Assessment Window: Work must be completed within a defined access window.

Scope of Work

• Complete a practitioner-level skills assessment used for validation and standard-setting purposes.
• Complete a short post-assessment survey providing feedback on the assessment experience.

This role does not include:

• Teaching or facilitation responsibilities
• Instructional or curriculum design work
• Content authoring or SME review of materials
• Ongoing advisory or consulting responsibilities

Responsibilities

• Complete a practitioner-level skills assessment used for validation and standard-setting purposes.
• Complete a short post-assessment survey providing feedback on the assessment experience.

Requirements

The Practitioner should be a current software engineer / developer with applied, real-world experience related to the following knowledge areas and skills:

Integrating Generative AI for Developers

• Develop a comprehensive technical implementation plan for integrating generative AI into existing systems
• Identify key technical requirements and dependencies for generative AI deployment
• Decide on architecture that supports scalable generative AI operations
• Create a phased rollout strategy to minimize disruption and manage risks
• Establish performance metrics and monitoring processes for generative AI systems

Aligning Generative AI with Business Cases

• Analyze business processes to identify opportunities for generative AI implementation
• Evaluate the potential ROI of generative AI applications across different business functions
• Develop use-case-specific strategies for integrating generative AI into product workflows
• Apply a framework for prioritizing generative AI initiatives based on business value and feasibility
• Design a pilot program to test generative AI in real-world contexts

Ensuring Interoperability in Generative AI Systems

• Assess the interoperability requirements for generative AI within an organization's technology ecosystem
• Create protocols to facilitate seamless integration with existing systems
• Develop strategies for managing version compatibility and updates across integrated AI systems
• Establish governance for maintaining interoperability as generative AI technologies evolve

Security for Generative AI Integrations

• Identify security vulnerabilities specific to generative AI
• Apply mitigations for various types of GenAI security vulnerabilities
• Develop strategies to protect against adversarial attacks and model manipulation
• Create incident response plans tailored to generative AI security breaches

Effective Cost Management for Generative AI

• Estimate the total cost of ownership for generative AI implementations
• Optimize computational resources for transfer learning models
• Evaluate the factors that impact Generative AI costs
• Apply techniques for reducing data storage and transfer costs associated with large/complex AI models
• Create budgeting and forecasting models for long-term generative AI initiatives

Scaling Integrated Generative AI

• Assess infrastructure requirements for supporting large-scale generative AI initiatives
• Design scalable architectures capable of handling increasing AI workloads and data volumes
• Implement load balancing and distributed computing strategies for integrated generative AI
• Build disaster recovery and product continuity plans specific to generative AI infrastructure

Applied Developer Workflow Integration

• Leverage AI-powered tools in day-to-day development workflows, including code generation, code completion, testing, and documentation

Nice-to-haves

• Active software engineer or developer with hands-on experience integrating generative AI into production systems or developer workflows.
• Practical, working knowledge of how the concepts listed above are applied in real professional settings.
• Does not need to be an academic researcher or industry thought leader — applied experience is what matters.